BACKGROUND: As radiotherapy techniques advance, so do planning methods for multi-target intracranial SRS cases. Multi-target-single-isocenter (MTSI) planning offers high-precision beam delivery with shortened duration. However, accommodating all targets in a single Patient-Specific-Quality-Assurance (PSQA) with QA devices like SRS MapCHECK (SRS MC) is generally impractical.
OBJECTIVE: Consequently, we conducted PSQA, using a custom script, by relocating each Target or Neighboring-Target-Group (T-NTG) relative to the beam isocenter on the PSQA device, ensuring each target\'s dose coverage at high precision.
METHODS: SRS treatment plans use 6MV-FFF beams, consisting of four Volumetric Modulated ARC Therapy (VMAT) arcs, including one full-arc and three half arcs with couch-kicks. A custom script calculated T-NTG coordinates relative to the beam isocenter. QA verification plans were created for each T-NTG, redefining the beam isocenter for precise alignment with the center of the SRS MC. CBCT images were acquired during PSQA for SRS MC alignment, and gamma-index analysis (GIA) was performed. A single-tail paired t-test assessed the passing rate (PR) for 75 QA verification plans.
RESULTS: GIA with l.0 mm/2.0% criteria for each QA plan yielded a PR > 95.5%, with an average of 98.9%. Plans achieving PR > 99.0% and > 97.0% constituted 63% and 92% of studied plans, respectively. Statistical significance was observed in a t-test with an ideal PR value of 100%, while insignificance was found with a PR value of 99%, suggesting that PSQA for individual targets consistently approaches 99% PR. In MTSI cases using 6MV-FFF beams, targets within the lateral dose-fall-off region require careful verification for acceptability. Our clinical study on individual T-NTG relocation demonstrates that the presented PSQA methods are generally acceptable, supported by a statistically insignificant PR against a 99% PR value.
CONCLUSIONS: Presented statistical analysis results indicate that the proposed PSQA approach can serve as a reliable tool in clinical settings.

BACKGROUND: Lattice radiation therapy (LRT) alternates regions of high and low doses within the target. The heterogeneous dose distribution is delivered to a geometrical structure of vertices segmented inside the tumor. LRT is typically used to treat patients with large tumor volumes with cytoreduction intent. Due to the geometric complexity of the target volume and the required dose distribution, LRT treatment planning demands additional resources, which may limit clinical integration.
OBJECTIVE: We introduce a fully automated method to (1) generate an ordered lattice of vertices with various sizes and center-to-center distances and (2) perform dose optimization and calculation. We aim to report the dosimetry associated with these lattices to help clinical decision-making.
METHODS: Sarcoma cancer patients with tumor volume between 100 cm3 and 1500 cm3 who received radiotherapy treatment between 2010 and 2018 at our institution were considered for inclusion. Automated segmentation and dose optimization/calculation were performed by using the Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices were modeled by spheres segmented within the gross tumor volume (GTV) with 1 cm/1.5 cm/2 cm diameters (LRT-1 cm/1.5 cm/2 cm) and 2 to 5 cm center-to-center distance on square lattices alternating along the superior-inferior direction. Organs at risk were modeled by subtracting the GTV from the body structure (body-GTV). The prescription dose was that 50% of the vertice volume should receive at least 20 Gy in one fraction. The automated dose optimization included three stages. The vertices optimization objectives were refined during optimization according to their values at the end of the first and second stages. Lattices were classified according to a score based on the minimization of body-GTV max dose and the maximization of GTV dose uniformity (measured with the equivalent uniform dose [EUD]), GTV dose heterogeneity (measured with the GTV D90%/D10% ratio), and the number of patients with more than one vertex inserted in the GTV. Plan complexity was measured with the modulation complexity score (MCS). Correlations were assessed with the Spearman correlation coefficient (r) and its associated p-value.
RESULTS: Thirty-three patients with GTV volumes between 150 and 1350 cm3 (median GTV volume = 494 cm3 , IQR = 272-779 cm3 were included. The median time required for segmentation/planning was 1 min/21 min. The number of vertices was strongly correlated with GTV volume in each LRT lattice for each center-to-center distance (r > 0.85, p-values < 0.001 in each case). Lattices with center-to-center distance = 2.5 cm/3 cm/3.5 cm in LRT-1.5 cm and center-to-center distance = 4 cm in LRT-1 cm had the best scores. These lattices were characterized by high heterogeneity (median GTV D90%/D10% between 0.06 and 0.19). The generated plans were moderately complex (median MCS ranged between 0.19 and 0.40).
CONCLUSIONS: The automated LRT planning method allows for the efficacious generation of vertices arranged in an ordered lattice and the refinement of planning objectives during dose optimization, enabling the systematic evaluation of LRT dosimetry from various lattice geometries.

OBJECTIVE: Volumetric modulated arc therapy (VMAT) is a widespread technique for the delivery of normo-fractionated radiation therapy (NFRT) and stereotactic body radiation therapy (SBRT). It is associated with a significant hardware burden requiring dose rate modulation, collimator movement and gantry rotation synchronisation. Patient specific quality assurance (PSQA) guarantees that the linacs can precisely and accurately deliver the planned dose. However, PSQA requires a significant time allocation and class solutions to reduce this while guaranteeing the deliverability of the plans should be investigated.
METHODS: In this study, an in-house developed Eclipse Scripting API (ESAPI) script was used to extract five independent plan complexity metrics from N = 667 VMAT treatment fields. The correlation between metrics and portal dosimetry measurements was investigated with Pearson correlation, box plot analysis and receiver operating characteristic curves, which were used to defined the best performing metric and its threshold.
RESULTS: The incidence of fields failing the clinical PSQA criteria of 3%/2mm (NFRT) and 3%/1.5mm (SBRT) was low (N = 1). The mean MLC opening was the metric with the highest correlation with the portal dosimetry data and among the best in discriminating the requirement of PSQA. The thresholds of 16.12 mm (NFRT) and 7.96 mm (SBRT) corresponded to true positive rates higher than 90%.
CONCLUSIONS: This work presents a quantitative approach to reduce the time allocation for PSQA by identifying the most complex plans demanding a dedicated measurement. The proposed method requires PSQA for approximately 10% of the plans. The ESAPI script is distributed open-source to ease the investigation and implementation at other institutions.

OBJECTIVE: Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application.
METHODS: A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan.
RESULTS: The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9\' 50″ ± 4\' 33″.
CONCLUSIONS: Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.

OBJECTIVE: An automated, in-vivo system to detect patient anatomy changes and machine output was developed using novel analysis of in-vivo electronic portal imaging device (EPID) images for every fraction of treatment on a Varian Halcyon. In-vivo approach identifies errors that go undetected by routine quality assurance (QA) to compliment daily machine performance check (MPC), with minimal physicist workload.
METHODS: Images for all fractions treated on a Halcyon were automatically downloaded and analyzed at the end of treatment day. For image analysis, compared to first fraction, the mean difference of high-dose region of interest is calculated. This metric has shown to predict changes in planning treatment volume (PTV) mean dose. Flags are raised for: (Type-A) treatment fraction whose mean difference exceeds 10%, to protect against large errors, and (Type-B) patients with three consecutive fractions with mean exceeding ±3%, to protect against systematic trends. If a threshold is exceeded, a physicist is e-mailed, a report for flagged patients, for investigation. To track machine output changes, for all patients treated on a day, the average and standard deviations are uploaded to a QA portal, along with the reviewed MPC, ensuring comprehensive QA for the Halcyon. To guide clinical implementation, a retrospective study from November 2017 till December 2020 was conducted, which grouped errors by treatment site. This framework has been used prospectively since January 2021.
RESULTS: From retrospective data of 1633 patients (35 759 fractions), no Type-A errors were found and only 45 patients (2.76%) had Type-B errors. These Type-B deviations were due to head-and-neck weight loss. For 6 months of prospective use (345 patients), 13 patients (3.7%) had Type-B errors and no Type-A errors.
CONCLUSIONS: This automated system protects against errors that can occur in vivo to provide a more comprehensive QA. This fully automated framework can be implemented in other centers with a Halcyon, requiring a desktop computer and analysis scripts.

BACKGROUND: This study presents a comprehensive collision avoidance framework based on three-dimension (3D) computer-aided design (CAD) modeling, a graphical user interface (GUI) as peripheral to the radiation treatment planning (RTP) environment, and patient-specific plan parameters for intensity-modulated proton therapy (IMPT).
METHODS: A stand-alone software application was developed leveraging the Varian scripting application programming interface (API) for RTP database object accessibility. The Collision Avoider software models the Hitachi ProBeat-V half gantry design and the Kuka robotic couch with triangle mesh structures. Patient-specific plan parameters are displayed in the collision avoidance software for potential proximity evaluation. The external surfaces of the patients and the immobilization devices are contoured based on computed tomography (CT) images. A \"table junction-to-CT-origin\" (JCT) measurement is made for every patient at the time of CT simulation to accurately provide reference location of the patient contours to the treatment couch. Collision evaluations were performed virtually with the program during treatment planning to prevent four major types of collisional events: collisions between the gantry head and the treatment couch, gantry head and the patient\'s body, gantry head and the robotic arm, and collisions between the gantry head and the immobilization devices.
RESULTS: The Collision Avoider software was able to accurately model the proton treatment delivery system and the robotic couch position. Commonly employed clinical beam configuration and JCT values were investigated. Brain and head and neck patients require more complex gantry and patient positioning system configurations. Physical measurements were performed to validate 3D CAD model geometry. Twelve clinical proton treatment plans were used to validate the accuracy of the software. The software can predict all four types of collisional events in our clinic since its full implementation in 2020.
CONCLUSIONS: A highly efficient patient-specific collision prevention program for scanning proton therapy has been successfully implemented. The graphical program has provided accurate collision detection since its inception at our institution.

OBJECTIVE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process.
METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period.
RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%.
CONCLUSIONS: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.

OBJECTIVE: The treatment couch position of a patient in external beam radiation therapy (EBRT) is usually acquired during initial treatment setup. This procedure has shown potential failure modes leading to near misses and adverse events in radiation treatment. This study aims to develop a method to automatically determine the couch position before setting up a patient for initial treatment.
METHODS: The Qfix couch-tops (kVue and DoseMax) have embedded reference marks (BBs) indicating its index levels and couch centerline. With the ESAPI, a C# script was programmed to automatically find the couch-top and embedded BBs in the planning CT and derive the treatment couch position according to treatment isocenter of a plan. Couch positions of EBRT plans with the kVue couch-top and SBRT plans using the DoseMax were calculated using the script. The calculation was evaluated by comparing calculated positions with couch coordinates captured during the initial treatment setup after image guidance. The calculations were further compared with daily treatment couch positions post image-guided adjustment for each treatment fraction.
RESULTS: For plans using the kVue couch-top for various treatment sites, the median (5-95 percentiles) differences between calculated and captured couch positions were 0.1 (-0.2 - 0.9), 0.5 (-1.1-2.0), 0.10 (-1.3-1.3) cm in the vertical, longitudinal, and lateral direction respectively. For the DoseMax couch-top, the median differences were 0.1 (-0.2-0.7), 0.2 (-0.3-1.1), and 0.2 (-0.7-0.9) cm in respective direction. The calculated positions were within 1 and 2 cm from the mean fraction positions for 95% patients on DoseMax and kVue couch-top respectively.
CONCLUSIONS: A method that automatically and accurately calculates treatment couch position from simulation CT was implemented in Varian Eclipse for Qfix couch-tops. This technique increases the efficiency of patient setup and enhances patient safety by reducing the risks of positioning errors.